Integraton of qualitative and quantitative operational risk data: a Bayesian approach

Source: Operational Risk Modelling and analysis book

Title: Integraton of qualitative and quantitative operational risk data: a Bayesian approach

Date: 2004

Author: P.Giudici

Operational risk and probabilistic networks. An application to corporate actions processing

Source: Infosys White Paper

Title: Operational risk and probabilistic networks. An application to corporate actions processing

Date: 2005

Author: S.Ramamurthy, H. Aror, A. Gosh

Methods of risk modeling in economic activities

Source: International Conference on Economics and Business Administration

Title: Methods of risk modeling in economic activities

Date: 2013

Author: S.V.Toma, C. Nistor, I-V.Alexa

Measuring operational risk using fuzzy logic modeling

Source: International Risk Management Institute

Title: Measuring operational risk using fuzzy logic modeling

Date: 2003

Author: S. Shah

This article describes how fuzzy logic modeling techniques can be used to assess operational risks when there is not enought historical data and it is not accurate information.

In classical set theory, the membership of elements in a set is assessed in binary terms according to a bivalent condition; that is to say, an element either belongs or does not belong to the set. In contrast, fuzzy sets theory are sets whose elements have degrees of membership.

The author provides with an example the steps for using this approach assess a risk on the top 10 list of a company.

These steps apply fuzzy logic techniques for developing a causal model that relates the risk to its key drivers or indicators. Then, the causal model is then used to develop a distribution of losses based on expectations for the levels of its key drivers.

These stpes are as follows:

1. Specify Key Risk Indicators. For each top risk, several key risk indicators (KRIs) are specified. 
2. Calibrate Fuzzy. Linguistic descriptors such as High, Low, Medium, Small, Large, for example, are assigned to a range of values for each KRI and the loss amount. These descriptors will form the basis for capturing expert input on the impact of KRIs on the loss amount. 
3. Specify Impact of KRIs on Loss Amount. Having specified the risk and its KRIs, the logical next step is to specify how the loss amount varies as a function of the KRIs. Experts provide fuzzy rules in the form of “if … then” statements that relate loss amounts to various levels of KRIs based on their knowledge and experience. 
4. Calculate Expected Loss Amount. Since the fuzzy rules cover all possible combinations of KRI levels, the estimated loss amount can be calculated for the current levels of each KRI. A fuzzy calculator applies the math based on the fuzzy rules to generate the expected loss. 
5. Calculate Distribution of Losses. A probability distribution of expected losses next year can be derived by representing the KRIs as a probability distribution of their levels expected next year.

Connectivity and measurement of operational risk: an input-output approach

Source: Soft Computing

Title: Connectivity and measurement of operational risk: an input-output approach

Date: 2003

Author: S. Scandizzo

The author proposes a model that focuses on cause side rather than effect side of the operational risks.

With this focus, these proposal estates that both externally and internally originated changes have effects among the different components of the organization. These effects cannot be separated: the combined effect of the changes will not be equal to the sum of the single effects, but will in general be greater.

Basically, it is an accounting framework that can be used as a framework for the analysis of the interdependencies within an institution.

Connectivity requires the modeling process to develop a ‘connectivity matrix’ that can then be used to estimate the likelihood of failure (or potential losses) for the process as a whole. And by estimating a “multiplier” created by the internal level of connectivity, it can also be a tool that can complement a statistical risk model.

To do so, it is necessary to identify the various components and the technology structure in each of their components.

The economic activities of a company can be broken up into a number of separate, but interactive individual cost centers. The data needed are flows of products and services amongst these cost centers.

We can construct this model by using the internal cost allocation model and transfer pricing information

Input–output models where firstly theorized by Leontiev and have ever since received wide attention and recognition in the world of economics. The framework is also called ‘‘inter-industry analysis’’ and focuses on modeling the interdependencies among industries in an economy.

This input-output model relies on the basic idea that as an additional unit of a product is demanded, a certain combination of other intermediate products is required to produce that unit. One of the interests in the field of input-output economics lies with the fact that it is very concrete in its use of empirical data and also very compact. All changes in the endogenous sectors of an input-output table are results of changes in the exogenous sectors.

The overall effect of these interdependencies is that a change in demand causes a change in the overall production. This is called the Leontiev multiplier.

It implies that:

• The total output depends on the final “sales” as well as on the interdependencies amongst the units.
• The impact of an external shock either a change in D (demand for one or more units’ output) or a change in M (as a consequence of technology, process or people change) cannot be understood unless all the existing linkages are taken into account.